Today I hope to give you the big vision behind the new inter­dis­ci­pli­nary field of com­pu­ta­tion­al sus­tain­abil­i­ty pio­neered at Cornell. Computers and com­pu­ta­tion­al think­ing have rev­o­lu­tion­ized our lives. The smart­phone is the ulti­mate exam­ple of a uni­ver­sal com­put­er. Apps trans­form the phone into dif­fer­ent devices. Unfortunately, the com­pu­ta­tion­al rev­o­lu­tion has done lit­tle for the sus­tain­abil­i­ty of our Earth. Yet, sus­tain­abil­i­ty prob­lems are unique in scale and com­plex­i­ty, often involv­ing sig­nif­i­cant com­pu­ta­tion­al chal­lenges.

Here is an exam­ple of a com­pu­ta­tion­al sus­tain­abil­i­ty approach for bird con­ser­va­tion. eBird is a cit­i­zen sci­ence pro­gram with over 300,000 vol­un­teer bird­ers who have sub­mit­ted over 300,000,000 bird obser­va­tions cor­re­spond­ing to more than 2,500 years of field work. This is excit­ing, but the chal­lenge is that the data are often biased, main­ly from urban areas.

To incen­tivize bird­ers to vis­it under­sam­pled loca­tions, we devel­oped a game, Avicaching, using game the­o­ry. Birders accrue Avicaching points toward a lot­tery for binoc­u­lars and oth­er prizes. The points are assigned to loca­tions based on the bird­ers’ behav­ior, to induce a more uni­form dis­tri­b­u­tion of bird obser­va­tions.

This has been a very suc­cess­ful game, shift­ing bird­ers to under­sam­pled loca­tions. We com­bined the eBird data with envi­ron­men­tal data, lots of data, and using advanced spa­tial and tem­po­ral machine learn­ing mod­els, and high‐performance com­put­ing, we are able to relate the envi­ron­men­tal pre­dic­tors with the pat­terns of occur­rence and absence of the species.

This ani­ma­tion shows the pat­terns of abun­dance of the north­ern pin­tail for dif­fer­ent months of the year, pro­duced by the machine learn­ing mod­els. The mod­els reveal at a fine res­o­lu­tion the habi­tat pref­er­ence of the birds, which allows for nov­el approach­es for bird con­ser­va­tion.

A good exam­ple of high‐precision con­ser­va­tion is bird returns, a pro­gram of The Nature Conservancy with the goal of pro­tect­ing migra­to­ry water­birds in California against the drought. The eBird mod­els iden­ti­fied the tar­get areas of the bird migra­tion in Sacramento Valley that are pro­vid­ed to The Nature Conservancy. Farmers sub­mit bids to The Nature Conservancy to keep the tar­get rice fields flood­ed in order to pro­vide habi­tat for the birds dur­ing the bird migra­tion.

This big data ana­lyt­ics and market‐based approach has gen­er­at­ed over twen­ty thou­sand acres of addi­tion­al habi­tat for water­birds in California. This is a rad­i­cal­ly nov­el way of doing bird con­ser­va­tion. It is only pos­si­ble because we use advanced com­pu­ta­tion­al meth­ods for high‐precision con­ser­va­tion.

Computational sus­tain­abil­i­ty aims to devel­op cross‐cutting com­pu­ta­tion­al approach­es. Findings can be trans­ferred across domains. This has been a key dri­ving force behind the dra­mat­ic advances in infor­ma­tion and com­pu­ta­tion tech­nol­o­gy, just like the uni­ver­sal com­put­er. For exam­ple, the game the­o­ry mod­el used for Avicaching is also used to decide where to place patrols to pre­vent poach­ing and ille­gal fish­ing. Computationally, these prob­lems are sim­i­lar.

We rep­re­sent these cross‐cutting com­pu­ta­tion­al themes with col­ored sub­way lines. Mechanism design and game the­o­ry is the baby blue line. I talked about these cir­cled projects. We have many oth­er com­pu­ta­tion­al sus­tain­abil­i­ty projects cov­er­ing a wide range of appli­ca­tion domains and com­pu­ta­tion­al themes.

Computational sus­tain­abil­i­ty is a tru­ly inter­dis­ci­pli­nary endeav­or since com­pu­ta­tion­al sus­tain­abil­i­ty encom­pass­es bal­anc­ing envi­ron­men­tal, eco­nom­ic, and soci­etal needs for human well‐being, for cur­rent and future gen­er­a­tions. A key chal­lenge is how to effec­tive­ly estab­lish the nec­es­sary large‐scale inter­dis­ci­pli­nary projects and col­lab­o­ra­tions. Computational sus­tain­abil­i­ty aims to advance com­pu­ta­tion­al meth­ods to help bal­ance eco­nom­ic, envi­ron­men­tal, and soci­etal needs for sus­tain­able devel­op­ment.

Computational sus­tain­abil­i­ty is a two‐way street. On one hand it injects com­pu­ta­tion­al think­ing that pro­vides new insights, method­olo­gies, and solu­tions to sus­tain­abil­i­ty prob­lems. On the oth­er hand, it leads to foun­da­tion­al con­tri­bu­tions to com­put­er sci­ence by expos­ing com­put­er sci­en­tists to new chal­lenge prob­lems and new for­malisms, and con­cepts from oth­er dis­ci­plines, lead­ing to cross‐cutting com­pu­ta­tion­al prob­lems in com­put­er sci­ence. More impor­tant­ly, it has tremen­dous soci­etal impact.